ordeal 0.1.20

Automated chaos testing for Python — fault injection, property assertions, and stateful exploration.

ordeal

Your tests pass. Your code still breaks.

Traditional tests verify what you thought of. Ordeal explores what you didn't — thousands of operation sequences, faults injected at every level, property assertions that hold across all of them. When ordeal passes, the code works. Not just on the happy path. Under adversity.

pip install ordeal

30-second example

from ordeal import ChaosTest, rule, invariant, always
from ordeal.faults import timing, numerical

class MyServiceChaos(ChaosTest):
    faults = [
        timing.timeout("myapp.api.call"),         # API times out
        numerical.nan_injection("myapp.predict"),  # model returns NaN
    ]

    @rule()
    def call_service(self):
        result = self.service.process("input")
        always(result is not None, "process never returns None")

    @invariant()
    def no_corruption(self):
        for item in self.service.results:
            always(not math.isnan(item), "no NaN in output")

TestMyServiceChaos = MyServiceChaos.TestCase

pytest --chaos                    # explore fault interleavings
pytest --chaos --chaos-seed 42    # reproduce exactly
ordeal explore                    # coverage-guided, reads ordeal.toml

You declare what can go wrong (faults), what your system does (rules), and what must stay true (assertions). Ordeal explores the combinations.

Why ordeal

Testing catches bugs you can imagine. The dangerous bugs are the ones you can't — a timeout during a retry, a NaN inside a recovery path, a permission error after the cache warmed up. These come from combinations, and the space of combinations is too large to explore by hand.

Ordeal automates this. It brings ideas from the most rigorous engineering cultures in the world to Python:

What	Idea	From
Stateful chaos testing with nemesis	An adversary toggles faults while Hypothesis explores interleavings	Jepsen + Hypothesis
Coverage-guided exploration	Save checkpoints at new code paths, branch from productive states	Antithesis
Property assertions	always, sometimes, reachable, unreachable — accumulate evidence across runs	Antithesis
Inline fault injection	buggify() — no-op in production, probabilistic fault in testing	FoundationDB
Boundary-biased generation	Test at 0, -1, empty, max-length — where bugs actually cluster	Jane Street QuickCheck
Mutation testing	Flip + to -, < to <= — verify your tests actually catch real bugs	Meta ACH
Differential testing	Compare two implementations on random inputs — catches regressions	Equivalence testing
Property mining	Discover invariants from execution traces — type, bounds, monotonicity	Specification mining

Read the full philosophy to understand why this matters.

The ordeal standard

When a project uses ordeal and passes, it means something:

• An explorer ran thousands of operation sequences with coverage guidance
• Faults were injected in combinations no human would write
• Property assertions held across all runs
• Mutations were caught

That's not "the tests pass." That's a certification that the code handles adversity. Ordeal-tested code is code you can trust.

Install

# From PyPI
pip install ordeal

# With extras
pip install ordeal[atheris]    # coverage-guided fuzzing via Atheris
pip install ordeal[api]        # API chaos testing via Schemathesis
pip install ordeal[all]        # everything

# As a CLI tool
uv tool install ordeal         # global install
uvx ordeal explore             # ephemeral, no install

# Development
git clone https://github.com/teilomillet/ordeal
cd ordeal && uv sync
uv run pytest                  # 205 tests

What's in the box

Stateful chaos testing

ChaosTest extends Hypothesis's RuleBasedStateMachine. You declare faults and rules — ordeal auto-injects a nemesis that toggles faults during exploration. The nemesis is just another Hypothesis rule, so the engine explores fault schedules like it explores any other state transition. Shrinking works automatically.

from ordeal import ChaosTest, rule, invariant
from ordeal.faults import io, numerical, timing

class StorageChaos(ChaosTest):
    faults = [
        io.error_on_call("myapp.storage.save", IOError),
        timing.intermittent_crash("myapp.worker.process", every_n=3),
        numerical.nan_injection("myapp.scoring.predict"),
    ]
    swarm = True  # random fault subsets per run — better aggregate coverage

    @rule()
    def write_data(self):
        self.service.save({"key": "value"})

    @rule()
    def read_data(self):
        result = self.service.load("key")
        always(result is not None, "reads never return None after write")

Swarm mode: each run activates a random subset of faults. Over many runs, this covers more fault combinations than always-all-on. Hypothesis handles the subset selection, so shrinking isolates the exact fault combination that triggers a failure.

Property assertions

Four types, each with different semantics:

from ordeal import always, sometimes, reachable, unreachable

always(len(results) > 0, "never empty")          # must hold every time — fails immediately
sometimes(cache_hit, "cache is used")             # must hold at least once — checked at session end
reachable("error-recovery-path")                  # code path must execute at least once
unreachable("silent-data-corruption")             # code path must never execute — fails immediately

always and unreachable fail instantly, triggering Hypothesis shrinking. sometimes and reachable accumulate evidence across the full session — they're checked when testing ends. This is the Antithesis assertion model, brought to Python.

Inline fault injection (BUGGIFY)

Place buggify() gates in your production code. They return False normally. During chaos testing, they probabilistically return True:

from ordeal.buggify import buggify, buggify_value

def process(data):
    if buggify():                                    # sometimes inject delay
        time.sleep(random.random() * 5)
    result = compute(data)
    return buggify_value(result, float('nan'))        # sometimes corrupt output

Seed-controlled. Thread-local. Zero-cost in production. This is FoundationDB's BUGGIFY for Python — the code under test is the test harness.

Coverage-guided exploration

The Explorer tracks which code paths each run discovers (AFL-style edge hashing). When a run finds new coverage, it saves a checkpoint. Future runs branch from high-value checkpoints, systematically exploring the state space:

from ordeal.explore import Explorer

explorer = Explorer(
    MyServiceChaos,
    target_modules=["myapp"],
    checkpoint_strategy="energy",  # favor productive checkpoints
)
result = explorer.run(max_time=60)
print(result.summary())
# Exploration: 5000 runs, 52000 steps, 60.0s
# Coverage: 287 edges, 43 checkpoints
# Failures found: 2
#   Run 342, step 15: ValueError (3 steps after shrinking)

Failures are shrunk — delta debugging removes unnecessary steps, then fault simplification removes unnecessary faults. You get the minimal sequence that reproduces the bug.

Scale with workers — each process gets a unique seed for independent exploration, results are aggregated:

explorer = Explorer(MyServiceChaos, target_modules=["myapp"], workers=8)

Configuration

# ordeal.toml — one file, human and machine readable
[explorer]
target_modules = ["myapp"]
max_time = 60
seed = 42
checkpoint_strategy = "energy"

[[tests]]
class = "tests.test_chaos:MyServiceChaos"

[report]
format = "both"
traces = true
verbose = true

See ordeal.toml.example for the full schema with every option documented.

QuickCheck with boundary bias

@quickcheck infers strategies from type hints. It biases toward boundary values — 0, -1, empty list, max length — where implementation bugs cluster:

from ordeal.quickcheck import quickcheck

@quickcheck
def test_sort_idempotent(xs: list[int]):
    assert sorted(sorted(xs)) == sorted(xs)

@quickcheck
def test_score_bounded(x: float, y: float):
    result = score(x, y)
    assert 0 <= result <= 1

Composable invariants

from ordeal.invariants import no_nan, no_inf, bounded, finite

valid_score = finite & bounded(0, 1)
valid_score(model_output)  # raises AssertionError with clear message

Invariants compose with &. Works with scalars, sequences, and numpy arrays.

Simulation primitives

Deterministic Clock and FileSystem — no mocks, no real I/O, instant:

from ordeal.simulate import Clock, FileSystem

clock = Clock()
fs = FileSystem()

clock.advance(3600)                      # instant — no real waiting
fs.inject_fault("/data.json", "corrupt") # reads return random bytes

Differential testing

Compare two implementations on the same random inputs — catches regressions and validates refactors:

from ordeal.diff import diff

result = diff(score_v1, score_v2, rtol=1e-6)
assert result.equivalent, result.summary()
# diff(score_v1, score_v2): 100 examples, EQUIVALENT

Mutation testing

Validates that your chaos tests actually catch bugs. If you flip + to - in the code and your tests still pass, your tests have a blind spot:

from ordeal.mutations import mutate_function_and_test

result = mutate_function_and_test("myapp.scoring.compute", my_tests)
print(result.summary())
# Mutation score: 15/18 (83%)
#   SURVIVED  L42:8 + -> -
#   SURVIVED  L67:4 negate if-condition

Fault library

from ordeal.faults import io, numerical, timing, network, concurrency

# I/O faults
io.error_on_call("mod.func")            # raise IOError
io.disk_full()                           # writes fail with ENOSPC
io.corrupt_output("mod.func")           # return random bytes
io.truncate_output("mod.func", 0.5)     # truncate to half

# Numerical faults
numerical.nan_injection("mod.func")      # output becomes NaN
numerical.inf_injection("mod.func")      # output becomes Inf
numerical.wrong_shape("mod.func", (1,512), (1,256))

# Timing faults
timing.timeout("mod.func")              # raise TimeoutError
timing.slow("mod.func", delay=2.0)      # add delay
timing.intermittent_crash("mod.func", every_n=3)
timing.jitter("mod.func", magnitude=0.01)

# Network faults
network.http_error("mod.client.post", status_code=503)
network.connection_reset("mod.client.post")
network.rate_limited("mod.client.get", retry_after=60)
network.dns_failure("mod.client.resolve")

# Concurrency faults
concurrency.contended_call("mod.pool.acquire", contention=0.1)
concurrency.thread_boundary("mod.cache.get")
concurrency.stale_state(service, "config", old_config)

Integrations

# Atheris — coverage-guided fuzzing steers buggify() decisions
from ordeal.integrations.atheris_engine import fuzz
fuzz(my_function, max_time=60)

# Schemathesis — API chaos testing
from ordeal.integrations.schemathesis_ext import chaos_api_test
chaos_api_test("http://localhost:8080/openapi.json", faults=[...])

Audit — justify adoption with data

ordeal audit myapp.scoring --test-dir tests/

myapp.scoring
  current:   33 tests |   343 lines | 98% coverage [verified]
  migrated:  12 tests |   130 lines | 96% coverage [verified]
  saving:   64% fewer tests | 62% less code | same coverage
  mined:    compute: output in [0, 1] (500/500, >=99% CI)
  mutation: 14/18 (78%)
  suggest:
    - L42 in compute(): test when x < 0
    - L67 in normalize(): test that ValueError is raised

Every number is either [verified] (measured via coverage.py JSON, cross-checked) or FAILED: reason — the audit never silently returns 0%. Mined properties include Wilson confidence intervals. When there are coverage gaps, it reads the source and tells you exactly what to test. Use --show-generated to inspect the test file, --save-generated to keep it.

CLI

ordeal audit myapp.scoring              # compare existing tests vs ordeal
ordeal explore                          # run from ordeal.toml
ordeal explore -w 8                     # parallel with 8 workers
ordeal explore -c ci.toml -v            # custom config, verbose
ordeal explore --max-time 300 --seed 99 # override settings
ordeal replay trace.json                # reproduce a failure
ordeal replay --shrink trace.json       # minimize a failure trace

Documentation

• Philosophy — Why ordeal exists and what it means for code quality
• Getting Started — Your first chaos test in 5 minutes
• Core Concepts — How ordeal thinks
• Guides — Explorer, simulation, mutations, integrations
• API Reference — Every function, every parameter
• Full docs

Architecture

ordeal/
├── chaos.py           ChaosTest + nemesis + swarm            Hypothesis + Jepsen
├── explore.py         Coverage-guided explorer               Antithesis
├── assertions.py      always/sometimes/reachable             Antithesis
├── buggify.py         Inline fault injection                 FoundationDB
├── quickcheck.py      @quickcheck + boundary bias            Jane Street
├── simulate.py        Clock, FileSystem                      Deterministic sim
├── invariants.py      Composable: no_nan & bounded(0,1)
├── mutations.py       AST mutation testing                   Meta ACH
├── trace.py           Trace recording + shrinking
├── config.py          TOML configuration
├── cli.py             ordeal explore / replay
├── plugin.py          pytest --chaos
├── strategies.py      Adversarial data generation
├── faults/            io, numerical, timing, network, concurrency
└── integrations/      atheris, schemathesis

License

Apache 2.0